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Lithium metal as a negative electrode material offers ten times the specific capacity of graphitic electrodes, but its rechargeable operation poses challenges like excessive and continuous interphase formation, high surface area lithium deposits and safety issues. Improving the lithium | electrolyte interface and interphase requires powerful surface analysis techniques, such as ToF-SIMS sputter depth profiling.This study investigates lithium metal sections with an SEI layer by ToF-SIMS using different sputter ions. An optimal sputter ion is chosen based on the measured ToF-SIMS sputter depth profiles and SEM analysis of the surface damage. Further, this method is adapted to lithium metal foil with an intermetallic coating. ToF-SIMS sputter depth profiles in both polarities provide comprehensive insights into the coating structure. Both investigations highlight the value of ToF-SIMS sputter depth profiling in lithium metal battery research and offer guidance for future studies. Optimizing lithium–electrolyte interfaces for Li metal batteries requires the use of powerful surface analysis techniques. Here, the authors demonstrate the value of ToF-SIMS sputter depth profiling in lithium metal battery research and offer guidance for future studies.

While autoregulative adaptation is a common feature of living tissues, only a few feedback‐controlled adaptive biomaterials are available so far. This paper herein reports a new polymer hydrogel platform designed to release anti‐inflammatory molecules in response to the inflammatory activation of human blood. In this system, anti‐inflammatory peptide drugs, targeting either the complement cascade, a complement receptor, or cyclophilin A, are conjugated to the hydrogel by a peptide sequence that is cleaved by elastase released from activated granulocytes. As a proof of concept, the adaptive drug delivery from the gel triggered by activated granulocytes and the effect of the released drug on the respective inflammatory pathways are demonstrated. Adjusting the gel functionalization degree is shown to allow for tuning the drug release profiles to effective doses within a micromolar range. Feedback‐controlled delivery of covalently conjugated drugs from a hydrogel matrix is concluded to provide valuable safety features suitable to equip medical devices with highly active anti‐inflammatory agents without suppressing the general immunosurveillance. A polymer hydrogel platform designed to release anti‐inflammatory molecules in response to the inflammatory activation of human blood is presented. Elastase from activated granulocytes releases covalently conjugated inhibitors against different steps of the inflammatory process from the hydrogel matrix. Suppression of the inflammation also terminates the elastase release from granulocytes and drug delivery ceases.

Successful translation of promising preclinical findings into clinical application remains challenging. To address the rising concerns of failing clinical trials and the resulting economic, social, and ethical consequences, preclinical confirmatory studies have been proposed to generate sufficiently robust evidence for guiding the decision-making process. In a unique funding call, 17 studies in Germany aimed to confirm exploratory findings across various biomedical research fields in a rigorously planned and executed multi-laboratory set-up. Alongside these preclinical research projects, a meta-research project was funded to provide methodological support and collectively investigate confirmatory study design and experimental outcomes. After the first four-year funding period, an in-person workshop brought together representatives from the preclinical confirmatory studies to discuss lessons learned. We summarize the outcomes of these stakeholder discussions, highlight common pitfalls, and propose optimization strategies for experimental set-up and project coordination. As a result, we advocate for new roles—such as preclinical research coordinators—and improved rules and regulations in preclinical research to facilitate large-scale academic research projects. Moreover, we highlight that diverse stakeholders must collaborate to effectively integrate confirmatory multi-laboratory studies into the preclinical research ecosystem.

Artificial intelligence (AI) and game-based methods such as serious games or gamification are both emerging technologies and methodologies in health care. The merging of the two could provide greater advantages, particularly in the field of therapeutic interventions in medicine. This scoping review sought to generate an overview of the currently existing literature on the connection of AI and game-based approaches in health care. The primary objectives were to cluster studies by disease and health topic addressed, level of care, and AI or games technology. For this scoping review, the databases PubMed, Scopus, IEEE Xplore, Cochrane Library, and PubPsych were comprehensively searched on February 2, 2022. Two independent authors conducted the screening process using Rayyan software (Rayyan Systems Inc). Only original studies published in English since 1992 were eligible for inclusion. The studies had to involve aspects of therapy or education in medicine and the use of AI in combination with game-based approaches. Each publication was coded for basic characteristics, including the population, intervention, comparison, and outcomes (PICO) criteria; the level of evidence; the disease and health issue; the level of care; the game variant; the AI technology; and the function type. Inductive coding was used to identify the patterns, themes, and categories in the data. Individual codings were analyzed and summarized narratively. A total of 16 papers met all inclusion criteria. Most of the studies (10/16, 63%) were conducted in disease rehabilitation, tackling motion impairment (eg, after stroke or trauma). Another cluster of studies (3/16, 19%) was found in the detection and rehabilitation of cognitive impairment. Machine learning was the main AI technology applied and serious games the main game-based approach used. However, direct interaction between the technologies occurred only in 3 (19%) of the 16 studies. The included studies all show very limited quality evidence. From the patients' and healthy individuals' perspective, generally high usability, motivation, and satisfaction were found. The review shows limited quality of evidence for the combination of AI and games in health care. Most of the included studies were nonrandomized pilot studies with few participants (14/16, 88%). This leads to a high risk for a range of biases and limits overall conclusions. However, the first results present a broad scope of possible applications, especially in motion and cognitive impairment, as well as positive perceptions by patients. In future, the development of adaptive game designs with direct interaction between AI and games seems promising and should be a topic for future reviews.

BackgroundThe complex nature of rheumatic diseases poses considerable challenges for clinicians when developing individualized treatment plans. Large language models (LLMs) such as ChatGPT could enable treatment decision support.ObjectiveTo compare treatment plans generated by ChatGPT-3.5 and GPT-4 to those of a clinical rheumatology board (RB).Design/methodsFictional patient vignettes were created and GPT-3.5, GPT-4, and the RB were queried to provide respective first- and second-line treatment plans with underlying justifications. Four rheumatologists from different centers, blinded to the origin of treatment plans, selected the overall preferred treatment concept and assessed treatment plans’ safety, EULAR guideline adherence, medical adequacy, overall quality, justification of the treatment plans and their completeness as well as patient vignette difficulty using a 5-point Likert scale.Results20 fictional vignettes covering various rheumatic diseases and varying difficulty levels were assembled and a total of 160 ratings were assessed. In 68.8% (110/160) of cases, raters preferred the RB’s treatment plans over those generated by GPT-4 (16.3%; 26/160) and GPT-3.5 (15.0%; 24/160). GPT-4’s plans were chosen more frequently for first-line treatments compared to GPT-3.5. No significant safety differences were observed between RB and GPT-4’s first-line treatment plans. Rheumatologists’ plans received significantly higher ratings in guideline adherence, medical appropriateness, completeness and overall quality. Ratings did not correlate with the vignette difficulty. LLM-generated plans were notably longer and more detailed.ConclusionGPT-4 and GPT-3.5 generated safe, high-quality treatment plans for rheumatic diseases, demonstrating promise in clinical decision support. Future research should investigate detailed standardized prompts and the impact of LLM usage on clinical decisions.

We present a nonlinear traffic flow network model that is coupled to gaseous hazard information for evacuation planning. This model is evaluated numerically against a linear network flow model for different objective functions that are relevant for evacuation problems. A numerical study shows the influence of the underlying evacuation models on the evacuation time as well as the exit strategies.

Background Therapeutic digital health applications (DiGAs) are expected to significantly enhance access to evidence-based care. Since 2020, German physicians and psychotherapists have been able to prescribe approved DiGAs, which are reimbursed by statutory health insurance. This study investigates the usage, knowledge and perception of DiGAs as well as the growing digitalization among internal medicine physicians in Germany. Methods A web-based survey was distributed at the 2024 annual congress of the German Society for Internal Medicine. Participants could respond by scanning a QR code or directly on a tablet. Results A total of 100 physicians completed the survey, with a mean age of 43.4 years. The majority were internal medicine physicians (85%). Of the respondents, 31% had already prescribed DiGAs, and 29% had tested one. Self-rated knowledge of DiGAs was low (median score 3.17/10). The main barriers identified were lack of knowledge about effective implementation (60%), lack of time for patient onboarding (27%), and concerns about patient adherence (21%). However, 92% believed that DiGAs could improve care, and 88% expressed interest in specific digital health training. The majority (64%) stated that digitalization had a positive impact on medical care and 39% of physicians expected their daily workload to decrease due to digitalization. In addition, 38% believed that the physician-patient relationship would improve as a result of digitalization. Conclusions While physicians widely acknowledged the potential benefits of DiGAs, adoption and understanding remain limited. Specific training in digital health is crucial to accelerate digitalization in internal medicine.

Ever since daptomycin was introduced to the clinic, daptomycin-resistant isolates have been reported. In most cases, the resistant isolates harbor point mutations in MprF, which produces and flips the positively charged phospholipid LysPG. This has led to the assumption that the resistance mechanism relies on the overproduction of LysPG, given that increased LysPG production may lead to increased electrostatic repulsion of positively charged antimicrobial compounds, including daptomycin. Here we show that the resistance mechanism is highly specific and relies on a different process that involves a functional MprF flippase, suggesting that the resistance-conferring mutations may enable the flippase to accommodate daptomycin or an unknown component that is crucial for its activity. Our report provides a new perspective on the mechanism of resistance to a major antibiotic. Daptomycin, a calcium-dependent lipopeptide antibiotic whose full mode of action is still not entirely understood, has become a standard-of-care agent for treating methicillin-resistant Staphylococcus aureus (MRSA) infections. Daptomycin-resistant (DAP-R) S. aureus mutants emerge during therapy, featuring isolates which in most cases possess point mutations in the mprF gene. MprF is a bifunctional bacterial resistance protein that synthesizes the positively charged lipid lysyl-phosphatidylglycerol (LysPG) and translocates it subsequently from the inner membrane leaflet to the outer membrane leaflet. This process leads to increased positive S. aureus surface charge and reduces susceptibility to cationic antimicrobial peptides and cationic antibiotics. We characterized the most commonly reported MprF mutations in DAP-R S. aureus strains in a defined genetic background and found that only certain mutations, including the frequently reported T345A single nucleotide polymorphism (SNP), can reproducibly cause daptomycin resistance. Surprisingly, T345A did not alter LysPG synthesis, LysPG translocation, or the S. aureus cell surface charge. MprF-mediated DAP-R relied on a functional flippase domain and was restricted to daptomycin and a related cyclic lipopeptide antibiotic, friulimicin B, suggesting that the mutations modulate specific interactions with these two antibiotics. Notably, the T345A mutation led to weakened intramolecular domain interactions of MprF, suggesting that daptomycin and friulimicin resistance-conferring mutations may alter the substrate range of the MprF flippase to directly translocate these lipopeptide antibiotics or other membrane components with crucial roles in the activity of these antimicrobials. Our study points to a new mechanism used by S. aureus to resist calcium-dependent lipopeptide antibiotics and increases our understanding of the bacterial phospholipid flippase MprF. IMPORTANCE Ever since daptomycin was introduced to the clinic, daptomycin-resistant isolates have been reported. In most cases, the resistant isolates harbor point mutations in MprF, which produces and flips the positively charged phospholipid LysPG. This has led to the assumption that the resistance mechanism relies on the overproduction of LysPG, given that increased LysPG production may lead to increased electrostatic repulsion of positively charged antimicrobial compounds, including daptomycin. Here we show that the resistance mechanism is highly specific and relies on a different process that involves a functional MprF flippase, suggesting that the resistance-conferring mutations may enable the flippase to accommodate daptomycin or an unknown component that is crucial for its activity. Our report provides a new perspective on the mechanism of resistance to a major antibiotic.

Remote patient monitoring (RPM) leverages advanced technology to monitor and manage patients’ health remotely and continuously. In 2022 European Alliance of Associations for Rheumatology (EULAR) points-to-consider for remote care were published to foster adoption of RPM, providing guidelines on where to position RPM in our practices. Sample papers and studies describe the value of RPM. But for many rheumatologists, the unanswered question remains the ‘how to?’ implement RPM.Using the successful, though not frictionless example of the Southmead rheumatology department, we address three types of barriers for the implementation of RPM: service, clinician and patients, with subsequent learning points that could be helpful for new teams planning to implement RPM. These address, but are not limited to, data governance, selecting high quality cost-effective solutions and ensuring compliance with data protection regulations. In addition, we describe five lacunas that could further improve RPM when addressed: establishing quality standards, creating a comprehensive database of available RPM tools, integrating data with electronic patient records, addressing reimbursement uncertainties and improving digital literacy among patients and healthcare professionals.

Large language models (LLMs), such as ChatGPT (Open AI), are increasingly used in medicine and supplement standard search engines as information sources. This leads to more \"consultations\" of LLMs about personal medical symptoms. This study aims to evaluate ChatGPT's performance in answering clinical case-based questions in otorhinolaryngology (ORL) in comparison to ORL consultants' answers. We used 41 case-based questions from established ORL study books and past German state examinations for doctors. The questions were answered by both ORL consultants and ChatGPT 3. ORL consultants rated all responses, except their own, on medical adequacy, conciseness, coherence, and comprehensibility using a 6-point Likert scale. They also identified (in a blinded setting) if the answer was created by an ORL consultant or ChatGPT. Additionally, the character count was compared. Due to the rapidly evolving pace of technology, a comparison between responses generated by ChatGPT 3 and ChatGPT 4 was included to give an insight into the evolving potential of LLMs. Ratings in all categories were significantly higher for ORL consultants (P<.001). Although inferior to the scores of the ORL consultants, ChatGPT's scores were relatively higher in semantic categories (conciseness, coherence, and comprehensibility) compared to medical adequacy. ORL consultants identified ChatGPT as the source correctly in 98.4% (121/123) of cases. ChatGPT's answers had a significantly higher character count compared to ORL consultants (P<.001). Comparison between responses generated by ChatGPT 3 and ChatGPT 4 showed a slight improvement in medical accuracy as well as a better coherence of the answers provided. Contrarily, neither the conciseness (P=.06) nor the comprehensibility (P=.08) improved significantly despite the significant increase in the mean amount of characters by 52.5% (n= (1470-964)/964; P<.001). While ChatGPT provided longer answers to medical problems, medical adequacy and conciseness were significantly lower compared to ORL consultants' answers. LLMs have potential as augmentative tools for medical care, but their \"consultation\" for medical problems carries a high risk of misinformation as their high semantic quality may mask contextual deficits.